Overview

Dataset statistics

Number of variables22
Number of observations814
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory146.3 KiB
Average record size in memory184.0 B

Variable types

Text2
DateTime1
Categorical6
Numeric13

Alerts

Abdominal Circumference (cm) is highly overall correlated with Waist-to-Height RatioHigh correlation
BMI is highly overall correlated with CVD Risk Score and 1 other fieldsHigh correlation
CVD Risk Score is highly overall correlated with BMIHigh correlation
Estimated LDL (mg/dL) is highly overall correlated with Total Cholesterol (mg/dL)High correlation
Total Cholesterol (mg/dL) is highly overall correlated with Estimated LDL (mg/dL)High correlation
Waist-to-Height Ratio is highly overall correlated with Abdominal Circumference (cm)High correlation
Weight (kg) is highly overall correlated with BMIHigh correlation
Family History of CVD is uniformly distributedUniform
Patient ID has unique valuesUnique

Reproduction

Analysis started2026-02-15 20:26:01.217890
Analysis finished2026-02-15 20:26:08.680148
Duration7.46 seconds
Software versionydata-profiling vv4.18.1
Download configurationconfig.json

Variables

Patient ID
Text

Unique 

Distinct814
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
2026-02-15T15:26:08.759260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters6.512
Distinct characters62
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique814 ?
Unique (%)100.0%

Sample

1st rowZMIC4650
2nd rowGvnF6714
3rd rowVRpd3736
4th rowTrTW1141
5th rowdJuC5084
ValueCountFrequency (%)
zmic46501
 
0.1%
gvnf67141
 
0.1%
vrpd37361
 
0.1%
trtw11411
 
0.1%
djuc50841
 
0.1%
bqrd87341
 
0.1%
rcgk40531
 
0.1%
wnwg06581
 
0.1%
rwhd86081
 
0.1%
zmqx92311
 
0.1%
Other values (804)804
98.8%
2026-02-15T15:26:08.899536image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7340
 
5.2%
1338
 
5.2%
9337
 
5.2%
8331
 
5.1%
0323
 
5.0%
6321
 
4.9%
4318
 
4.9%
3316
 
4.9%
2316
 
4.9%
5316
 
4.9%
Other values (52)3256
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)6512
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7340
 
5.2%
1338
 
5.2%
9337
 
5.2%
8331
 
5.1%
0323
 
5.0%
6321
 
4.9%
4318
 
4.9%
3316
 
4.9%
2316
 
4.9%
5316
 
4.9%
Other values (52)3256
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)6512
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7340
 
5.2%
1338
 
5.2%
9337
 
5.2%
8331
 
5.1%
0323
 
5.0%
6321
 
4.9%
4318
 
4.9%
3316
 
4.9%
2316
 
4.9%
5316
 
4.9%
Other values (52)3256
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)6512
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7340
 
5.2%
1338
 
5.2%
9337
 
5.2%
8331
 
5.1%
0323
 
5.0%
6321
 
4.9%
4318
 
4.9%
3316
 
4.9%
2316
 
4.9%
5316
 
4.9%
Other values (52)3256
50.0%
Distinct665
Distinct (%)81.7%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
Minimum2020-01-02 00:00:00
Maximum2025-12-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2026-02-15T15:26:08.982994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T15:26:09.052230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Sex
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
1
410 
0
404 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters814
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1410
50.4%
0404
49.6%

Length

2026-02-15T15:26:09.107553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-15T15:26:09.133302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1410
50.4%
0404
49.6%

Most occurring characters

ValueCountFrequency (%)
1410
50.4%
0404
49.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)814
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1410
50.4%
0404
49.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)814
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1410
50.4%
0404
49.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)814
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1410
50.4%
0404
49.6%

Age
Real number (ℝ)

Distinct57
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.670215
Minimum6.42
Maximum89.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.7 KiB
2026-02-15T15:26:09.180154image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6.42
5-th percentile30
Q137
median46
Q355
95-th percentile72
Maximum89.42
Range83
Interquartile range (IQR)18

Descriptive statistics

Standard deviation12.712717
Coefficient of variation (CV)0.27239466
Kurtosis-0.15120098
Mean46.670215
Median Absolute Deviation (MAD)9
Skewness0.52703087
Sum37989.555
Variance161.61318
MonotonicityNot monotonic
2026-02-15T15:26:09.222254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3230
 
3.7%
3730
 
3.7%
4927
 
3.3%
4827
 
3.3%
4327
 
3.3%
3126
 
3.2%
3326
 
3.2%
4026
 
3.2%
5525
 
3.1%
3825
 
3.1%
Other values (47)545
67.0%
ValueCountFrequency (%)
6.421
 
0.1%
256
 
0.7%
268
 
1.0%
276
 
0.7%
285
 
0.6%
296
 
0.7%
3024
2.9%
3126
3.2%
3230
3.7%
3326
3.2%
ValueCountFrequency (%)
89.421
 
0.1%
85.7151
 
0.1%
798
1.0%
782
 
0.2%
769
1.1%
759
1.1%
743
 
0.4%
733
 
0.4%
726
0.7%
713
 
0.4%

Weight (kg)
Real number (ℝ)

High correlation 

Distinct663
Distinct (%)81.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.243905
Minimum13.261
Maximum158.523
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.7 KiB
2026-02-15T15:26:09.263343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum13.261
5-th percentile53.3634
Q166.725
median87.572
Q3105.38775
95-th percentile117.25775
Maximum158.523
Range145.262
Interquartile range (IQR)38.66275

Descriptive statistics

Standard deviation21.937863
Coefficient of variation (CV)0.25437001
Kurtosis-0.81377992
Mean86.243905
Median Absolute Deviation (MAD)19.32
Skewness-0.12719732
Sum70202.539
Variance481.26983
MonotonicityNot monotonic
2026-02-15T15:26:09.314267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.45
 
0.6%
116.54
 
0.5%
116.24
 
0.5%
99.53
 
0.4%
97.83
 
0.4%
78.83
 
0.4%
117.83
 
0.4%
62.23
 
0.4%
73.33
 
0.4%
113.43
 
0.4%
Other values (653)780
95.8%
ValueCountFrequency (%)
13.2611
 
0.1%
15.0361
 
0.1%
19.5781
 
0.1%
21.3161
 
0.1%
46.650936021
 
0.1%
50.11
 
0.1%
50.21
 
0.1%
50.43
0.4%
50.4281
 
0.1%
50.51
 
0.1%
ValueCountFrequency (%)
158.5231
0.1%
149.8771
0.1%
124.36650421
0.1%
121.2005391
0.1%
1201
0.1%
119.91
0.1%
119.81
0.1%
119.71
0.1%
119.61
0.1%
119.5711
0.1%

BMI
Real number (ℝ)

High correlation 

Distinct520
Distinct (%)63.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.572945
Minimum5.184
Maximum53.028
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.7 KiB
2026-02-15T15:26:09.362356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum5.184
5-th percentile17.8
Q122.515
median28.3
Q333.975
95-th percentile40.305
Maximum53.028
Range47.844
Interquartile range (IQR)11.46

Descriptive statistics

Standard deviation7.3682502
Coefficient of variation (CV)0.25787507
Kurtosis-0.44548565
Mean28.572945
Median Absolute Deviation (MAD)5.77
Skewness0.24963526
Sum23258.377
Variance54.291111
MonotonicityNot monotonic
2026-02-15T15:26:09.406482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19.56
 
0.7%
31.86
 
0.7%
33.45
 
0.6%
285
 
0.6%
31.65
 
0.6%
20.35
 
0.6%
215
 
0.6%
27.25
 
0.6%
17.85
 
0.6%
22.85
 
0.6%
Other values (510)762
93.6%
ValueCountFrequency (%)
5.1841
0.1%
151
0.1%
15.11
0.1%
15.31
0.1%
15.42
0.2%
15.51
0.1%
15.61
0.1%
15.71
0.1%
15.81
0.1%
162
0.2%
ValueCountFrequency (%)
53.0281
0.1%
52.1921
0.1%
52.1361
0.1%
51.9841
0.1%
51.0221
0.1%
46.21
0.1%
45.61
0.1%
44.81
0.1%
44.71
0.1%
44.51
0.1%

Abdominal Circumference (cm)
Real number (ℝ)

High correlation 

Distinct575
Distinct (%)70.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.10519
Minimum49.542
Maximum136.336
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.7 KiB
2026-02-15T15:26:09.451157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum49.542
5-th percentile72.565
Q180.467
median91.85
Q3102.8
95-th percentile112.79235
Maximum136.336
Range86.794
Interquartile range (IQR)22.333

Descriptive statistics

Standard deviation13.457857
Coefficient of variation (CV)0.14611399
Kurtosis-0.53090692
Mean92.10519
Median Absolute Deviation (MAD)11.3395
Skewness0.23538809
Sum74973.625
Variance181.11392
MonotonicityNot monotonic
2026-02-15T15:26:09.499739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
76.36
 
0.7%
96.66
 
0.7%
86.96
 
0.7%
74.35
 
0.6%
77.44
 
0.5%
101.14
 
0.5%
108.74
 
0.5%
91.94
 
0.5%
74.24
 
0.5%
76.74
 
0.5%
Other values (565)767
94.2%
ValueCountFrequency (%)
49.5421
0.1%
702
0.2%
70.0911
0.1%
70.1841
0.1%
70.31
0.1%
70.41
0.1%
70.4111
0.1%
70.51
0.1%
70.62
0.2%
70.81
0.1%
ValueCountFrequency (%)
136.3361
0.1%
136.3191
0.1%
134.2971
0.1%
133.8461
0.1%
133.0651
0.1%
132.8611
0.1%
119.9961
0.1%
119.4951
0.1%
119.4931
0.1%
119.4841
0.1%
Distinct725
Distinct (%)89.1%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
2026-02-15T15:26:09.599022image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.9987715
Min length5

Characters and Unicode

Total characters4.883
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique643 ?
Unique (%)79.0%

Sample

1st row132/71
2nd row126/77
3rd row113/79
4th row106/87
5th row97/63
ValueCountFrequency (%)
127/843
 
0.4%
113/773
 
0.4%
143/673
 
0.4%
120/893
 
0.4%
111/973
 
0.4%
114/633
 
0.4%
139/813
 
0.4%
93/632
 
0.2%
105/642
 
0.2%
146/912
 
0.2%
Other values (715)787
96.7%
2026-02-15T15:26:09.737886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11181
24.2%
/814
16.7%
9430
 
8.8%
7370
 
7.6%
6368
 
7.5%
8339
 
6.9%
0328
 
6.7%
2296
 
6.1%
3284
 
5.8%
4259
 
5.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)4883
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11181
24.2%
/814
16.7%
9430
 
8.8%
7370
 
7.6%
6368
 
7.5%
8339
 
6.9%
0328
 
6.7%
2296
 
6.1%
3284
 
5.8%
4259
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4883
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11181
24.2%
/814
16.7%
9430
 
8.8%
7370
 
7.6%
6368
 
7.5%
8339
 
6.9%
0328
 
6.7%
2296
 
6.1%
3284
 
5.8%
4259
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4883
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11181
24.2%
/814
16.7%
9430
 
8.8%
7370
 
7.6%
6368
 
7.5%
8339
 
6.9%
0328
 
6.7%
2296
 
6.1%
3284
 
5.8%
4259
 
5.3%

Total Cholesterol (mg/dL)
Real number (ℝ)

High correlation 

Distinct230
Distinct (%)28.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean197.80883
Minimum1.817
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.7 KiB
2026-02-15T15:26:09.781231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.817
5-th percentile109
Q1149
median195.5
Q3252
95-th percentile290
Maximum300
Range298.183
Interquartile range (IQR)103

Descriptive statistics

Standard deviation59.001577
Coefficient of variation (CV)0.29827575
Kurtosis-0.95436573
Mean197.80883
Median Absolute Deviation (MAD)50.5
Skewness-0.0099806193
Sum161016.39
Variance3481.1861
MonotonicityNot monotonic
2026-02-15T15:26:09.827400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10910
 
1.2%
2909
 
1.1%
1479
 
1.1%
1798
 
1.0%
2038
 
1.0%
1928
 
1.0%
2968
 
1.0%
1508
 
1.0%
1608
 
1.0%
1057
 
0.9%
Other values (220)731
89.8%
ValueCountFrequency (%)
1.8171
 
0.1%
16.0881
 
0.1%
19.9321
 
0.1%
1004
0.5%
1014
0.5%
1022
 
0.2%
1033
0.4%
1045
0.6%
1057
0.9%
1063
0.4%
ValueCountFrequency (%)
3002
 
0.2%
2993
 
0.4%
2983
 
0.4%
2974
0.5%
2968
1.0%
295.39902571
 
0.1%
2957
0.9%
2942
 
0.2%
2934
0.5%
2921
 
0.1%

HDL (mg/dL)
Real number (ℝ)

Distinct65
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.718612
Minimum0.008
Maximum110.315
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.7 KiB
2026-02-15T15:26:09.878150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.008
5-th percentile32
Q142
median56
Q369
95-th percentile80
Maximum110.315
Range110.307
Interquartile range (IQR)27

Descriptive statistics

Standard deviation16.291488
Coefficient of variation (CV)0.29238861
Kurtosis-0.65826061
Mean55.718612
Median Absolute Deviation (MAD)14
Skewness-0.0094037809
Sum45354.95
Variance265.41257
MonotonicityNot monotonic
2026-02-15T15:26:09.925236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4422
 
2.7%
5621
 
2.6%
3221
 
2.6%
4621
 
2.6%
6121
 
2.6%
7820
 
2.5%
3819
 
2.3%
4019
 
2.3%
6319
 
2.3%
3618
 
2.2%
Other values (55)613
75.3%
ValueCountFrequency (%)
0.0081
 
0.1%
1.2761
 
0.1%
6.8091
 
0.1%
7.5421
 
0.1%
3015
1.8%
3117
2.1%
3221
2.6%
3315
1.8%
3414
1.7%
3517
2.1%
ValueCountFrequency (%)
110.3151
 
0.1%
895
0.6%
883
0.4%
875
0.6%
863
0.4%
853
0.4%
843
0.4%
834
0.5%
826
0.7%
815
0.6%

Fasting Blood Sugar (mg/dL)
Real number (ℝ)

Distinct136
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.50112
Minimum15.605
Maximum218.019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.7 KiB
2026-02-15T15:26:09.971504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15.605
5-th percentile73
Q191
median115
Q3138
95-th percentile175
Maximum218.019
Range202.414
Interquartile range (IQR)47

Descriptive statistics

Standard deviation31.666845
Coefficient of variation (CV)0.27181581
Kurtosis0.13554565
Mean116.50112
Median Absolute Deviation (MAD)23
Skewness0.41152218
Sum94831.909
Variance1002.7891
MonotonicityNot monotonic
2026-02-15T15:26:10.019184image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11515
 
1.8%
9215
 
1.8%
9614
 
1.7%
13014
 
1.7%
12514
 
1.7%
7014
 
1.7%
14813
 
1.6%
8613
 
1.6%
10412
 
1.5%
10712
 
1.5%
Other values (126)678
83.3%
ValueCountFrequency (%)
15.6051
 
0.1%
18.961
 
0.1%
19.0141
 
0.1%
21.2111
 
0.1%
23.8171
 
0.1%
7014
1.7%
716
0.7%
727
0.9%
7310
1.2%
744
 
0.5%
ValueCountFrequency (%)
218.0191
 
0.1%
215.6141
 
0.1%
213.6851
 
0.1%
212.9841
 
0.1%
1983
0.4%
1971
 
0.1%
1962
0.2%
1952
0.2%
1941
 
0.1%
1922
0.2%

Smoking Status
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
1
421 
0
393 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters814
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1421
51.7%
0393
48.3%

Length

2026-02-15T15:26:10.068823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-15T15:26:10.094518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1421
51.7%
0393
48.3%

Most occurring characters

ValueCountFrequency (%)
1421
51.7%
0393
48.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)814
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1421
51.7%
0393
48.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)814
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1421
51.7%
0393
48.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)814
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1421
51.7%
0393
48.3%

Diabetes Status
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
1
423 
0
391 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters814
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1423
52.0%
0391
48.0%

Length

2026-02-15T15:26:10.124850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-15T15:26:10.149794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1423
52.0%
0391
48.0%

Most occurring characters

ValueCountFrequency (%)
1423
52.0%
0391
48.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)814
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1423
52.0%
0391
48.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)814
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1423
52.0%
0391
48.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)814
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1423
52.0%
0391
48.0%
Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
2
287 
0
268 
1
259 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters814
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row2
5th row1

Common Values

ValueCountFrequency (%)
2287
35.3%
0268
32.9%
1259
31.8%

Length

2026-02-15T15:26:10.180757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-15T15:26:10.206590image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2287
35.3%
0268
32.9%
1259
31.8%

Most occurring characters

ValueCountFrequency (%)
2287
35.3%
0268
32.9%
1259
31.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)814
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2287
35.3%
0268
32.9%
1259
31.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)814
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2287
35.3%
0268
32.9%
1259
31.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)814
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2287
35.3%
0268
32.9%
1259
31.8%

Family History of CVD
Categorical

Uniform 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
1
407 
0
407 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters814
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1407
50.0%
0407
50.0%

Length

2026-02-15T15:26:10.240254image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-15T15:26:10.264382image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1407
50.0%
0407
50.0%

Most occurring characters

ValueCountFrequency (%)
1407
50.0%
0407
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)814
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1407
50.0%
0407
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)814
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1407
50.0%
0407
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)814
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1407
50.0%
0407
50.0%

Height (cm)
Real number (ℝ)

Distinct259
Distinct (%)31.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean175.67488
Minimum138.8
Maximum248.03974
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.7 KiB
2026-02-15T15:26:10.301778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum138.8
5-th percentile157.165
Q1166.525
median176
Q3184.1
95-th percentile195.605
Maximum248.03974
Range109.23974
Interquartile range (IQR)17.575

Descriptive statistics

Standard deviation12.35407
Coefficient of variation (CV)0.070323487
Kurtosis1.8730782
Mean175.67488
Median Absolute Deviation (MAD)9
Skewness0.47743862
Sum142999.35
Variance152.62305
MonotonicityNot monotonic
2026-02-15T15:26:10.348983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17628
 
3.4%
18124
 
2.9%
17023
 
2.8%
17423
 
2.8%
16323
 
2.8%
18922
 
2.7%
16221
 
2.6%
16621
 
2.6%
18220
 
2.5%
18719
 
2.3%
Other values (249)590
72.5%
ValueCountFrequency (%)
138.81
0.1%
1411
0.1%
146.34121481
0.1%
149.09979071
0.1%
150.31
0.1%
150.51
0.1%
150.62
0.2%
150.72
0.2%
150.74072391
0.1%
150.91
0.1%
ValueCountFrequency (%)
248.03974051
0.1%
239.49410081
0.1%
214.61
0.1%
214.11
0.1%
213.91
0.1%
212.55445981
0.1%
211.71
0.1%
2111
0.1%
207.97031351
0.1%
203.68411321
0.1%

Waist-to-Height Ratio
Real number (ℝ)

High correlation 

Distinct345
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52613278
Minimum0.259
Maximum0.804
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.7 KiB
2026-02-15T15:26:10.401122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.259
5-th percentile0.40265
Q10.45725
median0.523
Q30.58475
95-th percentile0.667
Maximum0.804
Range0.545
Interquartile range (IQR)0.1275

Descriptive statistics

Standard deviation0.085763025
Coefficient of variation (CV)0.16300643
Kurtosis-0.073483937
Mean0.52613278
Median Absolute Deviation (MAD)0.063
Skewness0.37496493
Sum428.27208
Variance0.0073552964
MonotonicityNot monotonic
2026-02-15T15:26:10.447987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5788
 
1.0%
0.4238
 
1.0%
0.5048
 
1.0%
0.4667
 
0.9%
0.4127
 
0.9%
0.5187
 
0.9%
0.537
 
0.9%
0.557
 
0.9%
0.4346
 
0.7%
0.5616
 
0.7%
Other values (335)743
91.3%
ValueCountFrequency (%)
0.2591
0.1%
0.2781
0.1%
0.361
0.1%
0.3651
0.1%
0.3661
0.1%
0.371
0.1%
0.3741
0.1%
0.3761
0.1%
0.3791
0.1%
0.382
0.2%
ValueCountFrequency (%)
0.8042
0.2%
0.7871
0.1%
0.7851
0.1%
0.7841
0.1%
0.7831
0.1%
0.7821
0.1%
0.7591
0.1%
0.7551
0.1%
0.7491
0.1%
0.7391
0.1%

Systolic BP
Real number (ℝ)

Distinct90
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean125.44226
Minimum90
Maximum179
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.7 KiB
2026-02-15T15:26:10.497336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum90
5-th percentile94
Q1108
median125
Q3140
95-th percentile165.35
Maximum179
Range89
Interquartile range (IQR)32

Descriptive statistics

Standard deviation21.406589
Coefficient of variation (CV)0.17064894
Kurtosis-0.50014359
Mean125.44226
Median Absolute Deviation (MAD)16
Skewness0.36948574
Sum102110
Variance458.24205
MonotonicityNot monotonic
2026-02-15T15:26:10.544573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11121
 
2.6%
13919
 
2.3%
10217
 
2.1%
12717
 
2.1%
11316
 
2.0%
11916
 
2.0%
14216
 
2.0%
13616
 
2.0%
11716
 
2.0%
12616
 
2.0%
Other values (80)644
79.1%
ValueCountFrequency (%)
909
1.1%
9110
1.2%
927
0.9%
9313
1.6%
9410
1.2%
9515
1.8%
968
1.0%
9710
1.2%
989
1.1%
9912
1.5%
ValueCountFrequency (%)
1794
0.5%
1784
0.5%
1773
0.4%
1762
0.2%
1753
0.4%
1743
0.4%
1732
0.2%
1724
0.5%
1714
0.5%
1702
0.2%

Diastolic BP
Real number (ℝ)

Distinct59
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.162162
Minimum60
Maximum119
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.7 KiB
2026-02-15T15:26:10.592576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile62
Q171
median83
Q393
95-th percentile110
Maximum119
Range59
Interquartile range (IQR)22

Descriptive statistics

Standard deviation14.558978
Coefficient of variation (CV)0.17506733
Kurtosis-0.5692337
Mean83.162162
Median Absolute Deviation (MAD)11
Skewness0.36544903
Sum67694
Variance211.96383
MonotonicityNot monotonic
2026-02-15T15:26:10.638763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9227
 
3.3%
7124
 
2.9%
6523
 
2.8%
8723
 
2.8%
9123
 
2.8%
7821
 
2.6%
6421
 
2.6%
6621
 
2.6%
7620
 
2.5%
7320
 
2.5%
Other values (49)591
72.6%
ValueCountFrequency (%)
6014
1.7%
6118
2.2%
6214
1.7%
6320
2.5%
6421
2.6%
6523
2.8%
6621
2.6%
6715
1.8%
6815
1.8%
6913
1.6%
ValueCountFrequency (%)
1196
0.7%
1185
0.6%
1174
0.5%
1164
0.5%
1156
0.7%
1145
0.6%
1135
0.6%
1123
0.4%
1112
 
0.2%
1106
0.7%

Estimated LDL (mg/dL)
Real number (ℝ)

High correlation 

Distinct226
Distinct (%)27.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114.13138
Minimum1
Maximum316.071
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.7 KiB
2026-02-15T15:26:10.682725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile26.65
Q163
median109
Q3165
95-th percentile208
Maximum316.071
Range315.071
Interquartile range (IQR)102

Descriptive statistics

Standard deviation60.648131
Coefficient of variation (CV)0.53138877
Kurtosis-0.62786369
Mean114.13138
Median Absolute Deviation (MAD)50.5
Skewness0.26584834
Sum92902.94
Variance3678.1959
MonotonicityNot monotonic
2026-02-15T15:26:10.731141image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9011
 
1.4%
5811
 
1.4%
17811
 
1.4%
18810
 
1.2%
1909
 
1.1%
1259
 
1.1%
919
 
1.1%
1939
 
1.1%
358
 
1.0%
408
 
1.0%
Other values (216)719
88.3%
ValueCountFrequency (%)
14
0.5%
62
0.2%
94
0.5%
102
0.2%
111
 
0.1%
123
0.4%
131
 
0.1%
154
0.5%
173
0.4%
183
0.4%
ValueCountFrequency (%)
316.0711
0.1%
311.2461
0.1%
308.5141
0.1%
306.9211
0.1%
300.2271
0.1%
298.4921
0.1%
2372
0.2%
2301
0.1%
2291
0.1%
2281
0.1%

CVD Risk Score
Real number (ℝ)

High correlation 

Distinct671
Distinct (%)82.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.357988
Minimum10.53
Maximum114.968
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.7 KiB
2026-02-15T15:26:10.776339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10.53
5-th percentile13.26965
Q115.209
median16.915
Q318.8375
95-th percentile21.3575
Maximum114.968
Range104.438
Interquartile range (IQR)3.6285

Descriptive statistics

Standard deviation10.807791
Coefficient of variation (CV)0.58872418
Kurtosis53.903188
Mean18.357988
Median Absolute Deviation (MAD)1.832
Skewness7.1767471
Sum14943.402
Variance116.80835
MonotonicityNot monotonic
2026-02-15T15:26:10.821784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
144
 
0.5%
15.454
 
0.5%
15.414
 
0.5%
17.054
 
0.5%
17.953
 
0.4%
18.623
 
0.4%
15.643
 
0.4%
17.173
 
0.4%
18.93
 
0.4%
18.553
 
0.4%
Other values (661)780
95.8%
ValueCountFrequency (%)
10.531
0.1%
10.861
0.1%
10.891
0.1%
11.111
0.1%
11.252
0.2%
11.31
0.1%
11.611
0.1%
11.8351
0.1%
11.951
0.1%
11.9951
0.1%
ValueCountFrequency (%)
114.9681
0.1%
114.1431
0.1%
112.3431
0.1%
104.2711
0.1%
104.0871
0.1%
104.0021
0.1%
98.651
0.1%
96.8071
0.1%
95.6841
0.1%
94.6291
0.1%

CVD Risk Level
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size12.7 KiB
2
391 
1
307 
0
116 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters814
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row1
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2391
48.0%
1307
37.7%
0116
 
14.3%

Length

2026-02-15T15:26:10.866852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2026-02-15T15:26:10.896809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2391
48.0%
1307
37.7%
0116
 
14.3%

Most occurring characters

ValueCountFrequency (%)
2391
48.0%
1307
37.7%
0116
 
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)814
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2391
48.0%
1307
37.7%
0116
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)814
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2391
48.0%
1307
37.7%
0116
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)814
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2391
48.0%
1307
37.7%
0116
 
14.3%

Interactions

2026-02-15T15:26:08.028768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T15:26:01.630260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T15:26:02.087042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T15:26:02.636226image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T15:26:03.178318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T15:26:03.686190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T15:26:04.214114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T15:26:04.780864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T15:26:05.320814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T15:26:06.021642image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T15:26:06.543647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T15:26:07.020240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T15:26:07.518485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T15:26:08.062031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T15:26:01.661143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T15:26:02.122981image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T15:26:02.671167image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T15:26:03.211650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T15:26:03.732517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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Correlations

2026-02-15T15:26:10.935609image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Abdominal Circumference (cm)AgeBMICVD Risk LevelCVD Risk ScoreDiabetes StatusDiastolic BPEstimated LDL (mg/dL)Family History of CVDFasting Blood Sugar (mg/dL)HDL (mg/dL)Height (cm)Physical Activity LevelSexSmoking StatusSystolic BPTotal Cholesterol (mg/dL)Waist-to-Height RatioWeight (kg)
Abdominal Circumference (cm)1.0000.0480.0550.0550.1250.1620.0660.0440.0420.024-0.008-0.0150.0400.0000.0400.0790.0400.8880.064
Age0.0481.0000.0330.1590.0560.0990.049-0.0200.0000.0880.0430.0300.0460.0000.0750.0700.0020.0280.032
BMI0.0550.0331.0000.1450.6030.0550.0430.0020.0000.029-0.001-0.1780.0640.0630.0210.006-0.0070.1210.649
CVD Risk Level0.0550.1590.1451.0000.0380.1540.1010.1610.2110.1120.1440.1220.1240.0100.2620.1390.1600.0770.124
CVD Risk Score0.1250.0560.6030.0381.0000.1820.1090.4390.0290.0940.028-0.0820.0000.0000.0000.4160.4510.1450.410
Diabetes Status0.1620.0990.0550.1540.1821.0000.0680.0590.0000.0830.0300.0000.0000.0000.0000.0000.0370.0820.098
Diastolic BP0.0660.0490.0430.1010.1090.0681.0000.1110.0000.066-0.0010.0310.0000.0000.0000.0580.1230.0430.055
Estimated LDL (mg/dL)0.044-0.0200.0020.1610.4390.0590.1111.0000.0700.012-0.1370.0200.0000.0000.0000.0040.9390.033-0.015
Family History of CVD0.0420.0000.0000.2110.0290.0000.0000.0701.0000.0000.0000.0930.0000.0380.0000.0000.0070.0000.000
Fasting Blood Sugar (mg/dL)0.0240.0880.0290.1120.0940.0830.0660.0120.0001.0000.0880.0320.0000.0740.0590.1040.033-0.0030.027
HDL (mg/dL)-0.0080.043-0.0010.1440.0280.030-0.001-0.1370.0000.0881.0000.0160.0000.0000.0000.0350.107-0.0210.012
Height (cm)-0.0150.030-0.1780.122-0.0820.0000.0310.0200.0930.0320.0161.0000.0000.0840.0000.0190.023-0.3630.050
Physical Activity Level0.0400.0460.0640.1240.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.000
Sex0.0000.0000.0630.0100.0000.0000.0000.0000.0380.0740.0000.0840.0001.0000.0110.0000.0000.0000.071
Smoking Status0.0400.0750.0210.2620.0000.0000.0000.0000.0000.0590.0000.0000.0000.0111.0000.0000.0000.0000.000
Systolic BP0.0790.0700.0060.1390.4160.0000.0580.0040.0000.1040.0350.0190.0000.0000.0001.0000.0180.0550.020
Total Cholesterol (mg/dL)0.0400.002-0.0070.1600.4510.0370.1230.9390.0070.0330.1070.0230.0000.0000.0000.0181.0000.027-0.024
Waist-to-Height Ratio0.8880.0280.1210.0770.1450.0820.0430.0330.000-0.003-0.021-0.3630.0000.0000.0000.0550.0271.0000.057
Weight (kg)0.0640.0320.6490.1240.4100.0980.055-0.0150.0000.0270.0120.0500.0000.0710.0000.020-0.0240.0571.000

Missing values

2026-02-15T15:26:08.556780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-02-15T15:26:08.634036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Patient IDDate of ServiceSexAgeWeight (kg)BMIAbdominal Circumference (cm)Blood Pressure (mmHg)Total Cholesterol (mg/dL)HDL (mg/dL)Fasting Blood Sugar (mg/dL)Smoking StatusDiabetes StatusPhysical Activity LevelFamily History of CVDHeight (cm)Waist-to-Height RatioSystolic BPDiastolic BPEstimated LDL (mg/dL)CVD Risk ScoreCVD Risk Level
0ZMIC4650November 29, 2021155.0111.90034.20095.200132/71129.078.0131.01111181.00.5261327121.018.0202
1GvnF671418 Apr 23050.0119.00044.20070.900126/77188.068.0113.01111164.00.4321267790.096.8072
2VRpd373607-03-2020047.062.20438.322108.822113/79164.076.0148.00001188.70.5771137958.016.5941
3TrTW114124/12/2024055.0112.80037.30080.900106/87145.032.0150.01121174.00.4651068783.017.6602
4dJuC5084December 02, 2020055.0106.00032.000109.30097/63287.035.0130.00110182.00.6019763222.018.9902
5bqRD873419 Dec 24143.068.89037.37298.302164/101276.063.0115.01110168.70.583164101183.023.1941
6RCGk4053August 07, 2023043.0106.00038.50092.500130/73195.031.0136.01010166.00.55713073134.018.1001
7WnwG06582023-04-18172.063.32128.71890.282132/76158.034.0175.01010172.20.5241327694.015.5042
8rwHD860830/06/2024036.055.40016.00086.20093/63109.050.0143.01110186.00.463936329.012.0301
9ZmqX923119 May 22055.0100.10033.10096.600115/73287.040.096.01010174.00.55511573217.018.1102
Patient IDDate of ServiceSexAgeWeight (kg)BMIAbdominal Circumference (cm)Blood Pressure (mmHg)Total Cholesterol (mg/dL)HDL (mg/dL)Fasting Blood Sugar (mg/dL)Smoking StatusDiabetes StatusPhysical Activity LevelFamily History of CVDHeight (cm)Waist-to-Height RatioSystolic BPDiastolic BPEstimated LDL (mg/dL)CVD Risk ScoreCVD Risk Level
810rlex766301/02/2020168.078.56132.46677.184141/76178.073.0143.01121141.000000.4520001417675.019.1032
811jiWa591504-08-2025135.064.83733.18188.896160/78152.059.0150.00021195.800000.4540001607863.017.6762
812CFVK419622 Apr 21165.085.44829.423105.400165/86289.076.0158.01011154.800000.68100016586183.019.9151
813xAzX673927/05/2020169.0104.38723.105114.05091/69167.047.0188.00001212.554460.661000916990.012.5112
814GhAr23202021-08-04132.098.30031.00093.000127/98261.042.094.01121178.000000.52200012798189.019.7702
815Usvw568406/02/2025126.083.85636.85277.15796/68140.032.0174.01101195.000000.396000966878.016.9702
816mhTL364107-19-2020161.057.84127.01570.902154/71280.077.0187.01000178.900000.39632215471173.018.7032
817ertp0641April 25, 2023139.0118.40039.10074.300101/92213.049.0140.00111174.000000.42700010192134.019.1302
818gdBF965503-28-2025168.019.57829.507103.497128/88134.045.0175.01000199.500000.5190001288859.014.9812
819rMUD91102022-10-10059.070.50027.200107.300122/94219.071.0140.01001161.000000.66600012294118.015.9202